Electronic program guides (EPGS) provide information to a television viewer regarding potential programs that are available for selection. The programs are typically delivered using a cable network or direct satellite broadcasting arrangement, or other well known video distribution methods. The purpose of the EPG is to inform the viewer of program options that are available for current or future selection, so as to ensure that the viewer is able to choose the program that they would most likely enjoy viewing.
The development of video delivery technologies offers an ever increasing number of choices to viewers and with this is a dilemma for the viewer. While more choices provide a greater opportunity for the viewer to select a desired program, it becomes more difficult for the viewer to determine what programs are available for viewing. The design of EPGs have attempted to adapt and “learn” what preferences a viewer has.
The prior art systems typically store data pertaining to the viewer's selections in the set-top-box, and analyze past viewing patterns to provide a recommendation in the form of a preferred list. Various algorithms can be defined for doing such, but fundamentally these approaches are based on using ‘intrinsic’ data derived from the viewer's habits. In such cases, the only extrinsic data received by the set top box is the data describing the program attributes. Typically, other forms of extrinsic data are not used for developing a preferred program list. However, using extrinsic data in conjunction with the intrinsic data can provide a more useful preferred viewing list of recommendations. Therefore, there is a need for systems and methods that develop and present a preferred viewing list of programs to the viewer using extrinsic data taking into account viewer preferences